Improved Reptile Search Algorithm for Optimal Design of Solar Photovoltaic Module

Abstract

This study focuses on the vital role of parameter extraction in optimizing and evaluating solar photovoltaic (PV) systems, as it directly influences their efficiency in converting solar energy to electricity. Researchers have extensively explored the application of various metaheuristic algorithms to accurately estimate solar PV parameters due to their crucial significance, leading to an extensive body of literature on the subject. However, the search for a robust and user-friendly optimizer with high convergence ability remains a challenging task that demands further research. To address this challenge, the study conducts a comprehensive comparative analysis of the RSALF optimizer, an innovative metaheuristic algorithm combining the reptile search algorithm (RSA) with Lévy flight (LF), for parameter extraction of PV model parameters using the Photowatt-PWP201 PV module as a case study. The experimental results demonstrate the RSALF optimizer's remarkable accuracy in parameter estimation, consistently yielding lower root mean square error values and closely aligning with experimental data. Moreover, comparative analysis with other recent optimization approaches highlights the RSALF optimizer's superiority, making it a promising tool for advancing the optimization of PV models and facilitating more efficient and sustainable solar energy utilization.